Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Sparse Spiking Gradient Descent
Authors: Nicolas Perez-Nieves, Dan Goodman
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the effectiveness of our method on real datasets of varying complexity (Fashion-MNIST, Neuromophic MNIST and Spiking Heidelberg Digits) achieving a speedup in the backward pass of up to 150x, and 85% more memory efficient, without losing accuracy. |
| Researcher Affiliation | Academia | Nicolas Perez-Nieves Electrical and Electronic Engineering Imperial College London London, United Kingdom EMAIL Dan F.M. Goodman Electrical and Electronic Engineering Imperial College London London, United Kingdom EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks found. |
| Open Source Code | No | No explicit statement or link providing open-source code for the methodology described in this paper. |
| Open Datasets | Yes | Fashion-MNIST dataset (F-MNIST) [44], Neuromorphic MNIST (N-MNIST) [45] dataset... Spiking Heidelberg Dataset (SHD) [46]) |
| Dataset Splits | No | No explicit details on train/validation/test splits are provided in the main text. It refers to Appendix E for training details, which is not available. |
| Hardware Specification | Yes | Figure 4 was obtained from running on an RTX6000 GPU. We also run this on smaller GPUs (GTX1060 and GTX1080Ti) |
| Software Dependencies | No | The paper mentions 'Pytorch CUDA extension' but does not specify version numbers for PyTorch or CUDA, nor any other software dependencies with versions. |
| Experiment Setup | No | The paper mentions a 'three-layer fully connected network' and the surrogate gradient function 'g(V ) := 1/(β|V Vth| + 1)2'. However, it states 'See Appendix E for all training details,' and Appendix E is not provided in the paper, hence complete experimental setup details like specific hyperparameters are missing from the main text. |